Deep learning has been all over the news lately. In a presentation I gave at Boston Data Festival 2013 and at a recent PyData Boston meetup I provided some history of the method and a sense of what it is being used for presently. This post aims to cover the first half of that presentation, focusing on the question of why we have been hearing so much about deep learning lately. The content is aimed at data scientists who might have heard a little about deep learning and are interested in a bit more context. Regardless of your background, hopefully you will see how deep learning might be relevant for you.

In North America, most veterinarians are general practitioners, and while specialists may be available by referral, veterinarians are often required to have expertise across many disciplines, species, and breeds. That's where cognitive computing comes in. LifeLearn, a Canadian veterinary technology company, is developing a cognitive computing system called Sofie (running on IBM Watson) that would give veterinarians access to extensive, up-to-date knowledge on animal diseases, their specific treatments, and develop individualized patient care plans. Sofie will allow veterinarians to pose freeform questions about animal diseases, search for potential conditions based on clinical presentation and breed predisposition, and explore current diagnostic and therapeutic options. All of which are updated frequently with the latest scientific literature.

Updated April 12, 2016 16:26:57 With oil and gas prices hovering at decade lows, companies are turning to artificial intelligence to cut costs and boost productivity. The technology, which gives companies the ability to predict future problems, is estimated to save the industry trillions of dollars and lead to a new wave of highly sophisticated jobs. At a time when the banking industry needs to become increasingly focused on creating better customer experiences, the importance of distributing personalized communications that provide real value has never been greater. Artificial intelligence (AI) can help make this possible - both automatically and at scale. The banking industry is undergoing a major transformation.

Computer scientists from the Michigan Institute of Technology (MIT) and a machine learning startup, PatternEx, have reportedly developed a new system that can correctly detect 85% of cyberattacks using artificial intelligence merged with input from human experts. At the moment, security systems are closely monitored by humans and programmed to pick up on cyberattacks that only follow very specific rules, as such missing any attacks that do not follow those rules. But, there are also systems autonomously run by computers that practice anomaly detection – i.e. the identification of items, events or observations – that do not conform to an expected pattern or other items in a dataset. This method often leads to false positives, meaning that humans doubt the reliability of the system and are forced to go back and check all the results anyway. To improve this, researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), in collaboration with PatternEx, have developed the AI2 artificial intelligent platform, which merges three different machine learning methods that enable computers to learn unsupervised.

The rise of both structured and unstructured data has created a booming market that is expected to be worth around 41.5 billion by 2018. The rapid growth of the big data market has resulted in the creation of a large crop of vendors that are all looking to take a slice. Amid the plethora of vendors competing for market position are a number of start-ups that are aiming to help organisations collect and analyse data. CBR identifies 10 companies that are worth watching. Founded in 2014, the company has over 30 million in capital raised so far from investors such as LinkedIn, Index Ventures, Benchmark Capital and The Data Collective.

Debugging web apps MIT researchers reported that they've developed a system that can quickly comb through tens of thousands of lines of application code to find security flaws by exploiting some peculiarities of the Ruby on Rails web programming framework. The team said that in tests on 50 popular web applications written using Ruby on Rails, the system found 23 previously undiagnosed security flaws, and it took no more than 64 seconds to analyze any given program. Daniel Jackson, professor in the Department of Electrical Engineering and Computer Science, said the system uses static analysis, which seeks to describe, in a very general way, how data flows through a program. "The classic example of this is if you wanted to do an abstract analysis of a program that manipulates integers, you might divide the integers into the positive integers, the negative integers, and zero." The static analysis would then evaluate every operation in the program according to its effect on integers' signs.

I don't know if you're in the market for a new car, of course, but chances are that soon, possibly the next time you buy a vehicle, it will have so much processing power and artificial intelligence that you won't won't be able to keep up. Because the smarter cars get, the safer we become. It's estimated that we could reduce traffic fatalities by 90% -- or 30,000 lives every year -- by 2050, once cars start driving themselves. To get there, tech companies are creating hardware and software that make semi-autonomous and fully autonomous cars a reality. NVIDIA (NASDAQ:NVDA) and Alphabet's (NASDAQ:GOOG) (NASDAQ:GOOGL) Google are two leaders in the car tech space -- and they're just getting started.

Nadine, a robot receptionist at the Nanyang Technological University (NTU), is staring at the visitor in front of her. "I remember you," she says. "You were here last Saturday." The long-haired, uncannily human-looking robot pauses as her software runs through past interactions to figure out the most appropriate thing to say. Finally, she settles on: "We talked about your job."

The purpose of this article is to clarify a few misconceptions about data and statistical science. I will start with a controversial statement: data science barely uses statistical science and techniques. The truth is actually more nuanced, as explained below. But the new statistical science in question is not regarded as statistics, by many statisticians. I don't know how to call it, "new statistical science" is a misnomer, because it is not all that novel.

A recent article titled "Why is AI Female?" made the connection that gendered labor, in service professions in particular, is fueling our expectations for gendered AI assistants and service robots. Furthermore, the author argues, this "feminizing -- and sexualizing -- of machines" signals a future with a disproportionate use of feminized VR and robots for a male-dominated sex industry. "Sex with robots is a big leap from asking Siri to set an alarm, but the fact that we've largely equated artificial intelligence with female personalities is worth examining. There are, after all, few sexualized male robots or avatars." Herbert Televox and Mr. Telelux, the early 20th century robots made by Westinghouse, were both male.